Cloud Digital Reporting for Compressor Failure Prediction
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Solution Overview
Problem
Current methods for monitoring and documenting the status of industrial and safety-critical equipment, such as medical gas compressors, lack standardization and often fail to capture essential technical parameters, making it difficult to accurately assess equipment status and predict potential failures, leading to inefficient maintenance and compliance issues.
Innovation Solution
A system that converts controller data from equipment into digital reports stored on a cloud-based online portal, utilizing artificial intelligence to predict failure events and automate corrective actions, while ensuring technician interaction for data upload and alerting mechanisms to prevent unnoticed issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection and documentation methods are used, then technicians can perform equipment checks, but the process lacks standardization and requires significant time for documentation and compliance verification
Solution Approach 1:
The patent creates digital copies of equipment inspection data by automatically capturing sensor readings and controller data, storing them in standardized digital formats in the cloud. This eliminates manual documentation while preserving all inspection information in a searchable, compliant format.
Solution Approach 2:
The patent replaces manual mechanical documentation processes with automated electronic data capture and storage systems. Controllers and sensors automatically transmit data to cloud-based databases, eliminating the need for physical clipboards, manual signing, and paper-based compliance tracking.
2Measurement precision
If traditional monitoring methods are used, then equipment status can be checked periodically, but essential technical parameters are often omitted or inadequately captured
Solution Approach 1:
The patent implements a universal data collection framework that standardizes the capture of all essential technical parameters across different equipment types. The system uses standardized data models and schemas that ensure consistent collection of vibration, temperature, pressure, and other critical parameters regardless of the specific equipment being monitored.
Solution Approach 2:
The system continuously monitors equipment parameters and provides feedback through automated alerts and notifications when parameters deviate from normal ranges. This real-time feedback loop ensures that critical information is captured and acted upon promptly, preventing parameter omissions that could lead to equipment failure.
3Ease of operation
If cloud-based digital reporting is implemented, then data standardization and accessibility improve, but system complexity and data security requirements increase
Solution Approach 1:
The patent introduces a standardized API layer and data translation service that acts as an intermediary between diverse equipment controllers and the cloud-based portal. This intermediary handles data format conversion, authentication, and security protocols, simplifying the interface for end users while managing the underlying system complexity.
4Reliability
If more comprehensive data collection is implemented, then predictive maintenance capability improves, but data processing requirements and computational resources increase
Solution Approach 1:
The patent segments data processing into multiple levels: edge devices perform initial data filtering and preprocessing, regional servers conduct intermediate analysis, and cloud-based AI models perform comprehensive predictive maintenance analysis. This segmentation reduces the computational burden on any single system while maintaining high prediction accuracy through distributed processing.
Data Source
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AI summary
A method, computer program product, and computer system for converting controller data uploaded to the cloud to digital report stored on the cloud and accessible via an online portal. One or more data packets from a controller of a compressor are received over a network, the one or more data packets including data associated with technical parameters of the compressor at a given state of the compressor. The data contained in the data packets are converted to a digital report of the given state of the compressor in a human readable format. The digital reports are stored in one or more databases accessible through an online portal associated with the computer system. One or more artificial intelligence models and machine learning techniques are leveraged to improve failure event predicting and corrective action response time for technical system, using the digital reports as input datasets.